Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations1120
Missing cells12
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory298.4 KiB
Average record size in memory272.8 B

Variable types

Numeric15
Categorical1
Text1

Alerts

% DPT is highly overall correlated with % Hepatitis B and 4 other fieldsHigh correlation
% Hepatitis B is highly overall correlated with % DPT and 4 other fieldsHigh correlation
% VOP is highly overall correlated with % DPT and 4 other fieldsHigh correlation
%BCG is highly overall correlated with % DPT and 10 other fieldsHigh correlation
%Hib is highly overall correlated with % DPT and 4 other fieldsHigh correlation
%Triple Viral is highly overall correlated with % DPT and 4 other fieldsHigh correlation
BCG is highly overall correlated with %BCG and 7 other fieldsHigh correlation
CODIGO is highly overall correlated with DPTOHigh correlation
DPT is highly overall correlated with %BCG and 7 other fieldsHigh correlation
DPTO is highly overall correlated with CODIGOHigh correlation
Hepatitis B is highly overall correlated with %BCG and 7 other fieldsHigh correlation
Hib is highly overall correlated with %BCG and 7 other fieldsHigh correlation
Población de 1 año (1) is highly overall correlated with BCG and 6 other fieldsHigh correlation
Población menor de 1 año (1) is highly overall correlated with BCG and 6 other fieldsHigh correlation
Triple Viral is highly overall correlated with %BCG and 7 other fieldsHigh correlation
VOP is highly overall correlated with %BCG and 7 other fieldsHigh correlation
Población menor de 1 año (1) is highly skewed (γ1 = 23.96266317) Skewed
VOP is highly skewed (γ1 = 22.00247277) Skewed
DPT is highly skewed (γ1 = 22.00359739) Skewed
BCG is highly skewed (γ1 = 21.40805538) Skewed
Hepatitis B is highly skewed (γ1 = 22.00371927) Skewed
Hib is highly skewed (γ1 = 22.01679291) Skewed
Población de 1 año (1) is highly skewed (γ1 = 23.85890016) Skewed
Triple Viral is highly skewed (γ1 = 21.62583105) Skewed
CODIGO has unique values Unique

Reproduction

Analysis started2025-04-10 14:00:16.510595
Analysis finished2025-04-10 14:00:28.646279
Duration12.14 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

CODIGO
Real number (ℝ)

High correlation  Unique 

Distinct1120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38755.279
Minimum5001
Maximum99773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:28.713950image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5345.7
Q115679.75
median25844
Q366577.5
95-th percentile85280.05
Maximum99773
Range94772
Interquartile range (IQR)50897.75

Descriptive statistics

Standard deviation26582.665
Coefficient of variation (CV)0.68591081
Kurtosis-1.1254837
Mean38755.279
Median Absolute Deviation (MAD)20184.5
Skewness0.44993325
Sum43405912
Variance7.0663806 × 108
MonotonicityStrictly increasing
2025-04-10T09:00:28.780400image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5001 1
 
0.1%
52390 1
 
0.1%
52435 1
 
0.1%
52427 1
 
0.1%
52418 1
 
0.1%
52411 1
 
0.1%
52405 1
 
0.1%
52399 1
 
0.1%
52385 1
 
0.1%
52480 1
 
0.1%
Other values (1110) 1110
99.1%
ValueCountFrequency (%)
5001 1
0.1%
5002 1
0.1%
5004 1
0.1%
5021 1
0.1%
5030 1
0.1%
5031 1
0.1%
5034 1
0.1%
5036 1
0.1%
5038 1
0.1%
5040 1
0.1%
ValueCountFrequency (%)
99773 1
0.1%
99624 1
0.1%
99524 1
0.1%
99001 1
0.1%
97889 1
0.1%
97777 1
0.1%
97666 1
0.1%
97511 1
0.1%
97161 1
0.1%
97001 1
0.1%

DPTO
Categorical

High correlation 

Distinct33
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size82.3 KiB
Antioquia
125 
Boyacá
123 
Cundinamarca
116 
Santander
87 
Nariño
64 
Other values (28)
605 

Length

Max length15
Median length11
Mean length7.7080357
Min length4

Characters and Unicode

Total characters8633
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAntioquia
2nd rowAntioquia
3rd rowAntioquia
4th rowAntioquia
5th rowAntioquia

Common Values

ValueCountFrequency (%)
Antioquia 125
 
11.2%
Boyacá 123
 
11.0%
Cundinamarca 116
 
10.4%
Santander 87
 
7.8%
Nariño 64
 
5.7%
Tolima 47
 
4.2%
Bolívar 45
 
4.0%
Cauca 42
 
3.8%
Valle 42
 
3.8%
Norte Santander 40
 
3.6%
Other values (23) 389
34.7%

Length

2025-04-10T09:00:28.832690image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santander 127
 
10.8%
antioquia 125
 
10.6%
boyacá 123
 
10.4%
cundinamarca 116
 
9.8%
nariño 64
 
5.4%
tolima 47
 
4.0%
bolívar 45
 
3.8%
cauca 42
 
3.6%
valle 42
 
3.6%
norte 40
 
3.4%
Other values (26) 407
34.6%

Most occurring characters

ValueCountFrequency (%)
a 1646
19.1%
n 719
 
8.3%
i 595
 
6.9%
o 564
 
6.5%
r 532
 
6.2%
u 441
 
5.1%
t 397
 
4.6%
c 372
 
4.3%
d 360
 
4.2%
e 358
 
4.1%
Other values (34) 2649
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7394
85.6%
Uppercase Letter 1179
 
13.7%
Space Separator 58
 
0.7%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1646
22.3%
n 719
9.7%
i 595
 
8.0%
o 564
 
7.6%
r 532
 
7.2%
u 441
 
6.0%
t 397
 
5.4%
c 372
 
5.0%
d 360
 
4.9%
e 358
 
4.8%
Other values (17) 1410
19.1%
Uppercase Letter
ValueCountFrequency (%)
C 305
25.9%
B 169
14.3%
A 168
14.2%
S 155
13.1%
N 104
 
8.8%
M 59
 
5.0%
V 52
 
4.4%
T 47
 
4.0%
H 37
 
3.1%
G 28
 
2.4%
Other values (5) 55
 
4.7%
Space Separator
ValueCountFrequency (%)
58
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8573
99.3%
Common 60
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1646
19.2%
n 719
 
8.4%
i 595
 
6.9%
o 564
 
6.6%
r 532
 
6.2%
u 441
 
5.1%
t 397
 
4.6%
c 372
 
4.3%
d 360
 
4.2%
e 358
 
4.2%
Other values (32) 2589
30.2%
Common
ValueCountFrequency (%)
58
96.7%
. 2
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8273
95.8%
None 360
 
4.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1646
19.9%
n 719
 
8.7%
i 595
 
7.2%
o 564
 
6.8%
r 532
 
6.4%
u 441
 
5.3%
t 397
 
4.8%
c 372
 
4.5%
d 360
 
4.4%
e 358
 
4.3%
Other values (29) 2289
27.7%
None
ValueCountFrequency (%)
á 163
45.3%
í 66
18.3%
ñ 64
 
17.8%
ó 59
 
16.4%
é 8
 
2.2%
Distinct1039
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size84.9 KiB
2025-04-10T09:00:28.978465image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length27
Median length23
Mean length8.9758929
Min length3

Characters and Unicode

Total characters10053
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique978 ?
Unique (%)87.3%

Sample

1st rowMedellín
2nd rowAbejorral
3rd rowAbriaquí
4th rowAlejandría
5th rowAmagá
ValueCountFrequency (%)
san 86
 
5.4%
de 56
 
3.5%
la 52
 
3.3%
el 46
 
2.9%
puerto 30
 
1.9%
del 22
 
1.4%
cd 20
 
1.3%
santa 20
 
1.3%
juan 10
 
0.6%
carmen 9
 
0.6%
Other values (1027) 1242
78.0%
2025-04-10T09:00:29.312762image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1601
15.9%
o 674
 
6.7%
e 625
 
6.2%
r 607
 
6.0%
n 595
 
5.9%
i 576
 
5.7%
l 477
 
4.7%
473
 
4.7%
u 388
 
3.9%
t 360
 
3.6%
Other values (50) 3677
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7979
79.4%
Uppercase Letter 1514
 
15.1%
Space Separator 473
 
4.7%
Open Punctuation 32
 
0.3%
Close Punctuation 32
 
0.3%
Other Punctuation 22
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1601
20.1%
o 674
 
8.4%
e 625
 
7.8%
r 607
 
7.6%
n 595
 
7.5%
i 576
 
7.2%
l 477
 
6.0%
u 388
 
4.9%
t 360
 
4.5%
c 267
 
3.3%
Other values (20) 1809
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 217
14.3%
S 212
14.0%
P 157
10.4%
A 103
 
6.8%
M 97
 
6.4%
L 88
 
5.8%
B 88
 
5.8%
T 78
 
5.2%
G 73
 
4.8%
E 60
 
4.0%
Other values (15) 341
22.5%
Space Separator
ValueCountFrequency (%)
473
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Other Punctuation
ValueCountFrequency (%)
. 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9493
94.4%
Common 560
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1601
16.9%
o 674
 
7.1%
e 625
 
6.6%
r 607
 
6.4%
n 595
 
6.3%
i 576
 
6.1%
l 477
 
5.0%
u 388
 
4.1%
t 360
 
3.8%
c 267
 
2.8%
Other values (45) 3323
35.0%
Common
ValueCountFrequency (%)
473
84.5%
( 32
 
5.7%
) 32
 
5.7%
. 22
 
3.9%
- 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9674
96.2%
None 379
 
3.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1601
16.5%
o 674
 
7.0%
e 625
 
6.5%
r 607
 
6.3%
n 595
 
6.2%
i 576
 
6.0%
l 477
 
4.9%
473
 
4.9%
u 388
 
4.0%
t 360
 
3.7%
Other values (43) 3298
34.1%
None
ValueCountFrequency (%)
á 122
32.2%
í 103
27.2%
ó 74
19.5%
é 47
 
12.4%
ñ 22
 
5.8%
ú 10
 
2.6%
Á 1
 
0.3%

Población menor de 1 año (1)
Real number (ℝ)

High correlation  Skewed 

Distinct661
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean764.67321
Minimum0
Maximum118045
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:29.442405image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1138
median283
Q3557.75
95-th percentile2013.75
Maximum118045
Range118045
Interquartile range (IQR)419.75

Descriptive statistics

Standard deviation3988.0136
Coefficient of variation (CV)5.2153174
Kurtosis677.51228
Mean764.67321
Median Absolute Deviation (MAD)173.5
Skewness23.962663
Sum856434
Variance15904252
MonotonicityNot monotonic
2025-04-10T09:00:29.528760image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 8
 
0.7%
172 7
 
0.6%
53 7
 
0.6%
218 6
 
0.5%
97 6
 
0.5%
66 6
 
0.5%
246 6
 
0.5%
86 6
 
0.5%
112 6
 
0.5%
127 6
 
0.5%
Other values (651) 1056
94.3%
ValueCountFrequency (%)
0 1
0.1%
8 1
0.1%
17 1
0.1%
21 1
0.1%
22 1
0.1%
23 1
0.1%
28 2
0.2%
29 1
0.1%
31 2
0.2%
32 1
0.1%
ValueCountFrequency (%)
118045 1
0.1%
35489 1
0.1%
29085 1
0.1%
20991 1
0.1%
17460 1
0.1%
11256 1
0.1%
10195 1
0.1%
9272 1
0.1%
8769 1
0.1%
8603 1
0.1%

VOP
Real number (ℝ)

High correlation  Skewed 

Distinct611
Distinct (%)54.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean710.4361
Minimum0
Maximum111145
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:29.594448image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1108
median224
Q3479.5
95-th percentile2061.8
Maximum111145
Range111145
Interquartile range (IQR)371.5

Descriptive statistics

Standard deviation3929.4532
Coefficient of variation (CV)5.5310437
Kurtosis577.68595
Mean710.4361
Median Absolute Deviation (MAD)145
Skewness22.002473
Sum794978
Variance15440602
MonotonicityNot monotonic
2025-04-10T09:00:29.670271image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 8
 
0.7%
152 7
 
0.6%
40 7
 
0.6%
299 7
 
0.6%
45 7
 
0.6%
80 7
 
0.6%
102 6
 
0.5%
159 6
 
0.5%
147 6
 
0.5%
84 6
 
0.5%
Other values (601) 1052
93.9%
ValueCountFrequency (%)
0 3
0.3%
1 1
 
0.1%
2 1
 
0.1%
4 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
13 1
 
0.1%
15 1
 
0.1%
17 2
0.2%
ValueCountFrequency (%)
111145 1
0.1%
44980 1
0.1%
29428 1
0.1%
26623 1
0.1%
21772 1
0.1%
12014 1
0.1%
9710 1
0.1%
8822 1
0.1%
8688 1
0.1%
7959 1
0.1%

% VOP
Real number (ℝ)

High correlation 

Distinct1058
Distinct (%)94.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean84.42178
Minimum0
Maximum229.59641
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:29.731020image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.440746
Q168.085749
median83.227561
Q397.988439
95-th percentile129.69245
Maximum229.59641
Range229.59641
Interquartile range (IQR)29.902691

Descriptive statistics

Standard deviation26.838818
Coefficient of variation (CV)0.31791344
Kurtosis2.3802141
Mean84.42178
Median Absolute Deviation (MAD)14.993079
Skewness0.68208003
Sum94467.971
Variance720.32216
MonotonicityNot monotonic
2025-04-10T09:00:29.787262image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7
 
0.6%
83.33333333 4
 
0.4%
75 4
 
0.4%
72.72727273 4
 
0.4%
85.13513514 3
 
0.3%
83.72093023 3
 
0.3%
0 3
 
0.3%
80.85106383 3
 
0.3%
90.43478261 3
 
0.3%
93.61702128 2
 
0.2%
Other values (1048) 1083
96.7%
ValueCountFrequency (%)
0 3
0.3%
2.702702703 1
 
0.1%
6.451612903 1
 
0.1%
7.54332314 1
 
0.1%
11.76470588 1
 
0.1%
17.39130435 1
 
0.1%
21.45748988 1
 
0.1%
22.6640159 1
 
0.1%
24.72324723 1
 
0.1%
25 1
 
0.1%
ValueCountFrequency (%)
229.5964126 1
0.1%
217.4706649 1
0.1%
200.4524887 1
0.1%
192.2815946 1
0.1%
189.8016997 1
0.1%
176.2557078 1
0.1%
170.5696203 1
0.1%
169.6832579 1
0.1%
166.3043478 1
0.1%
165.5688623 1
0.1%

DPT
Real number (ℝ)

High correlation  Skewed 

Distinct614
Distinct (%)54.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean710.73905
Minimum0
Maximum110967
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:29.848130image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1108
median226
Q3479.5
95-th percentile2042.7
Maximum110967
Range110967
Interquartile range (IQR)371.5

Descriptive statistics

Standard deviation3922.0281
Coefficient of variation (CV)5.5182392
Kurtosis577.9981
Mean710.73905
Median Absolute Deviation (MAD)146
Skewness22.003597
Sum795317
Variance15382304
MonotonicityNot monotonic
2025-04-10T09:00:29.907618image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148 9
 
0.8%
40 8
 
0.7%
39 8
 
0.7%
62 7
 
0.6%
45 6
 
0.5%
161 6
 
0.5%
166 6
 
0.5%
49 6
 
0.5%
102 6
 
0.5%
146 6
 
0.5%
Other values (604) 1051
93.8%
ValueCountFrequency (%)
0 3
0.3%
1 1
 
0.1%
2 1
 
0.1%
4 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
15 1
 
0.1%
17 1
 
0.1%
ValueCountFrequency (%)
110967 1
0.1%
44523 1
0.1%
29907 1
0.1%
26622 1
0.1%
21506 1
0.1%
12014 1
0.1%
9710 1
0.1%
8822 1
0.1%
8688 1
0.1%
8084 1
0.1%

% DPT
Real number (ℝ)

High correlation 

Distinct1058
Distinct (%)94.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean84.599966
Minimum0
Maximum247.53363
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:29.969393image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.796
Q168.192292
median83.333333
Q398.122945
95-th percentile129.49565
Maximum247.53363
Range247.53363
Interquartile range (IQR)29.930653

Descriptive statistics

Standard deviation26.935352
Coefficient of variation (CV)0.3183849
Kurtosis2.7631
Mean84.599966
Median Absolute Deviation (MAD)15.039448
Skewness0.72568419
Sum94667.362
Variance725.51319
MonotonicityNot monotonic
2025-04-10T09:00:30.027189image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7
 
0.6%
50 4
 
0.4%
72.72727273 3
 
0.3%
80 3
 
0.3%
90.43478261 3
 
0.3%
90.90909091 3
 
0.3%
0 3
 
0.3%
78.18930041 2
 
0.2%
65.11627907 2
 
0.2%
78.57142857 2
 
0.2%
Other values (1048) 1087
97.1%
ValueCountFrequency (%)
0 3
0.3%
2.702702703 1
 
0.1%
6.451612903 1
 
0.1%
7.747196738 1
 
0.1%
11.76470588 1
 
0.1%
17.39130435 1
 
0.1%
21.65991903 1
 
0.1%
22.86282306 1
 
0.1%
24.35424354 1
 
0.1%
25.86206897 2
0.2%
ValueCountFrequency (%)
247.5336323 1
0.1%
217.4706649 1
0.1%
200.4524887 1
0.1%
194.8261238 1
0.1%
189.8016997 1
0.1%
176.2557078 1
0.1%
170.5696203 1
0.1%
169.6832579 1
0.1%
165.5688623 1
0.1%
164.9681529 1
0.1%

BCG
Real number (ℝ)

High correlation  Skewed 

Distinct562
Distinct (%)50.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean713.96157
Minimum0
Maximum122304
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:30.084497image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q158.5
median156
Q3399
95-th percentile2141.7
Maximum122304
Range122304
Interquartile range (IQR)340.5

Descriptive statistics

Standard deviation4364.6029
Coefficient of variation (CV)6.1132182
Kurtosis555.64025
Mean713.96157
Median Absolute Deviation (MAD)120
Skewness21.408055
Sum798923
Variance19049759
MonotonicityNot monotonic
2025-04-10T09:00:30.146803image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 11
 
1.0%
20 10
 
0.9%
8 9
 
0.8%
9 9
 
0.8%
1 9
 
0.8%
16 8
 
0.7%
47 8
 
0.7%
21 8
 
0.7%
0 8
 
0.7%
46 8
 
0.7%
Other values (552) 1031
92.1%
ValueCountFrequency (%)
0 8
0.7%
1 9
0.8%
2 5
0.4%
3 5
0.4%
4 6
0.5%
5 7
0.6%
6 3
 
0.3%
7 7
0.6%
8 9
0.8%
9 9
0.8%
ValueCountFrequency (%)
122304 1
0.1%
41663 1
0.1%
41556 1
0.1%
31809 1
0.1%
22339 1
0.1%
13754 1
0.1%
12760 1
0.1%
12258 1
0.1%
11045 1
0.1%
10850 1
0.1%

%BCG
Real number (ℝ)

High correlation 

Distinct1064
Distinct (%)95.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean63.17296
Minimum0
Maximum341.79104
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:30.202079image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.6508539
Q137.241015
median57.845433
Q380.967994
95-th percentile136.83167
Maximum341.79104
Range341.79104
Interquartile range (IQR)43.726979

Descriptive statistics

Standard deviation38.980732
Coefficient of variation (CV)0.61704774
Kurtosis3.9034155
Mean63.17296
Median Absolute Deviation (MAD)21.534084
Skewness1.3284125
Sum70690.542
Variance1519.4974
MonotonicityNot monotonic
2025-04-10T09:00:30.260221image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.7%
50 5
 
0.4%
66.66666667 4
 
0.4%
40 3
 
0.3%
38.0952381 3
 
0.3%
47.5 3
 
0.3%
27.90697674 3
 
0.3%
59.09090909 3
 
0.3%
41.86046512 2
 
0.2%
43.30708661 2
 
0.2%
Other values (1054) 1083
96.7%
ValueCountFrequency (%)
0 8
0.7%
1.333333333 1
 
0.1%
1.492537313 1
 
0.1%
1.515151515 1
 
0.1%
1.639344262 2
 
0.2%
2.127659574 1
 
0.1%
2.272727273 1
 
0.1%
2.325581395 1
 
0.1%
2.631578947 1
 
0.1%
2.877697842 1
 
0.1%
ValueCountFrequency (%)
341.7910448 1
0.1%
265.2317881 1
0.1%
259.6412556 1
0.1%
225 1
0.1%
214.9934811 1
0.1%
208.0482897 1
0.1%
198.9963504 1
0.1%
191.0676533 1
0.1%
187.0646766 1
0.1%
186.4531198 1
0.1%

Hepatitis B
Real number (ℝ)

High correlation  Skewed 

Distinct620
Distinct (%)55.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean712.22431
Minimum0
Maximum111130
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:30.319621image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1108
median226
Q3479
95-th percentile2039.2
Maximum111130
Range111130
Interquartile range (IQR)371

Descriptive statistics

Standard deviation3927.3179
Coefficient of variation (CV)5.5141587
Kurtosis578.16202
Mean712.22431
Median Absolute Deviation (MAD)147
Skewness22.003719
Sum796979
Variance15423826
MonotonicityNot monotonic
2025-04-10T09:00:30.374652image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148 8
 
0.7%
39 8
 
0.7%
40 8
 
0.7%
152 7
 
0.6%
146 6
 
0.5%
161 6
 
0.5%
80 6
 
0.5%
102 6
 
0.5%
89 6
 
0.5%
86 6
 
0.5%
Other values (610) 1052
93.9%
ValueCountFrequency (%)
0 3
0.3%
1 1
 
0.1%
2 1
 
0.1%
4 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
15 1
 
0.1%
17 1
 
0.1%
ValueCountFrequency (%)
111130 1
0.1%
44529 1
0.1%
29881 1
0.1%
26660 1
0.1%
21506 1
0.1%
12014 1
0.1%
9711 1
0.1%
8822 1
0.1%
8688 1
0.1%
8187 1
0.1%

% Hepatitis B
Real number (ℝ)

High correlation 

Distinct1057
Distinct (%)94.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean84.81212
Minimum0
Maximum247.53363
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:30.432519image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.777825
Q168.192292
median83.499547
Q398.286865
95-th percentile131.10352
Maximum247.53363
Range247.53363
Interquartile range (IQR)30.094574

Descriptive statistics

Standard deviation27.208132
Coefficient of variation (CV)0.32080476
Kurtosis2.6693522
Mean84.81212
Median Absolute Deviation (MAD)15.142987
Skewness0.74110119
Sum94904.762
Variance740.28242
MonotonicityNot monotonic
2025-04-10T09:00:30.564982image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 6
 
0.5%
50 4
 
0.4%
0 3
 
0.3%
90.43478261 3
 
0.3%
90.90909091 3
 
0.3%
72.72727273 3
 
0.3%
40 3
 
0.3%
78.57142857 3
 
0.3%
70 3
 
0.3%
88 2
 
0.2%
Other values (1047) 1086
97.0%
ValueCountFrequency (%)
0 3
0.3%
2.702702703 1
 
0.1%
6.451612903 1
 
0.1%
7.747196738 1
 
0.1%
11.76470588 1
 
0.1%
17.39130435 1
 
0.1%
21.65991903 1
 
0.1%
22.86282306 1
 
0.1%
24.35424354 1
 
0.1%
25.86206897 2
0.2%
ValueCountFrequency (%)
247.5336323 1
0.1%
217.4706649 1
0.1%
200.4524887 1
0.1%
194.0627651 1
0.1%
189.8016997 1
0.1%
176.2557078 1
0.1%
170.5696203 1
0.1%
169.6832579 1
0.1%
165.5688623 1
0.1%
164.9681529 1
0.1%

Hib
Real number (ℝ)

High correlation  Skewed 

Distinct616
Distinct (%)55.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean710.68275
Minimum0
Maximum111070
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:30.620797image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1108
median225
Q3482.5
95-th percentile2040.9
Maximum111070
Range111070
Interquartile range (IQR)374.5

Descriptive statistics

Standard deviation3924.5581
Coefficient of variation (CV)5.5222363
Kurtosis578.59876
Mean710.68275
Median Absolute Deviation (MAD)146
Skewness22.016793
Sum795254
Variance15402156
MonotonicityNot monotonic
2025-04-10T09:00:30.675002image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148 9
 
0.8%
39 8
 
0.7%
40 7
 
0.6%
62 7
 
0.6%
34 6
 
0.5%
146 6
 
0.5%
102 6
 
0.5%
166 6
 
0.5%
45 6
 
0.5%
80 6
 
0.5%
Other values (606) 1052
93.9%
ValueCountFrequency (%)
0 3
0.3%
1 1
 
0.1%
2 1
 
0.1%
4 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
15 1
 
0.1%
17 1
 
0.1%
ValueCountFrequency (%)
111070 1
0.1%
44523 1
0.1%
29907 1
0.1%
26622 1
0.1%
21506 1
0.1%
12014 1
0.1%
9710 1
0.1%
8822 1
0.1%
8688 1
0.1%
8084 1
0.1%

%Hib
Real number (ℝ)

High correlation 

Distinct1053
Distinct (%)94.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean84.568046
Minimum0
Maximum247.53363
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:30.964159image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.796
Q168.215095
median83.333333
Q398.122945
95-th percentile129.49565
Maximum247.53363
Range247.53363
Interquartile range (IQR)29.90785

Descriptive statistics

Standard deviation26.974461
Coefficient of variation (CV)0.31896753
Kurtosis2.7504356
Mean84.568046
Median Absolute Deviation (MAD)15.016502
Skewness0.71873996
Sum94631.644
Variance727.62153
MonotonicityNot monotonic
2025-04-10T09:00:31.029816image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7
 
0.6%
50 4
 
0.4%
78.57142857 3
 
0.3%
0 3
 
0.3%
90.90909091 3
 
0.3%
90.43478261 3
 
0.3%
72.72727273 3
 
0.3%
95.12195122 2
 
0.2%
78.18930041 2
 
0.2%
75.6097561 2
 
0.2%
Other values (1043) 1087
97.1%
ValueCountFrequency (%)
0 3
0.3%
2.702702703 1
 
0.1%
6.451612903 1
 
0.1%
7.747196738 1
 
0.1%
11.76470588 1
 
0.1%
17.39130435 1
 
0.1%
21.65991903 1
 
0.1%
22.86282306 1
 
0.1%
24.35424354 1
 
0.1%
24.47761194 1
 
0.1%
ValueCountFrequency (%)
247.5336323 1
0.1%
217.4706649 1
0.1%
200.4524887 1
0.1%
194.8261238 1
0.1%
189.8016997 1
0.1%
176.2557078 1
0.1%
170.5696203 1
0.1%
169.6832579 1
0.1%
165.5688623 1
0.1%
164.9681529 1
0.1%

Población de 1 año (1)
Real number (ℝ)

High correlation  Skewed 

Distinct654
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean764.45089
Minimum0
Maximum117581
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:31.083340image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q1138
median281
Q3553.5
95-th percentile2016
Maximum117581
Range117581
Interquartile range (IQR)415.5

Descriptive statistics

Standard deviation3979.4215
Coefficient of variation (CV)5.2055947
Kurtosis672.81907
Mean764.45089
Median Absolute Deviation (MAD)171
Skewness23.8589
Sum856185
Variance15835796
MonotonicityNot monotonic
2025-04-10T09:00:31.146725image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131 8
 
0.7%
43 7
 
0.6%
219 6
 
0.5%
222 6
 
0.5%
217 5
 
0.4%
190 5
 
0.4%
161 5
 
0.4%
258 5
 
0.4%
37 5
 
0.4%
45 5
 
0.4%
Other values (644) 1063
94.9%
ValueCountFrequency (%)
0 1
 
0.1%
8 1
 
0.1%
16 1
 
0.1%
21 2
0.2%
23 1
 
0.1%
25 1
 
0.1%
27 1
 
0.1%
31 2
0.2%
32 1
 
0.1%
34 3
0.3%
ValueCountFrequency (%)
117581 1
0.1%
35395 1
0.1%
29314 1
0.1%
21302 1
0.1%
17552 1
0.1%
11292 1
0.1%
10317 1
0.1%
9418 1
0.1%
8944 1
0.1%
8614 1
0.1%

Triple Viral
Real number (ℝ)

High correlation  Skewed 

Distinct622
Distinct (%)55.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean723.82752
Minimum0
Maximum110882
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:31.211561image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.9
Q1112.5
median230
Q3511.5
95-th percentile2014.3
Maximum110882
Range110882
Interquartile range (IQR)399

Descriptive statistics

Standard deviation3954.0172
Coefficient of variation (CV)5.462651
Kurtosis560.02379
Mean723.82752
Median Absolute Deviation (MAD)145
Skewness21.625831
Sum809963
Variance15634252
MonotonicityNot monotonic
2025-04-10T09:00:31.283591image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 7
 
0.6%
98 7
 
0.6%
143 6
 
0.5%
169 6
 
0.5%
130 6
 
0.5%
42 6
 
0.5%
77 6
 
0.5%
118 6
 
0.5%
31 6
 
0.5%
152 6
 
0.5%
Other values (612) 1057
94.4%
ValueCountFrequency (%)
0 2
0.2%
4 1
0.1%
7 1
0.1%
10 1
0.1%
12 1
0.1%
13 1
0.1%
15 1
0.1%
16 1
0.1%
17 1
0.1%
18 2
0.2%
ValueCountFrequency (%)
110882 1
0.1%
45907 1
0.1%
31500 1
0.1%
25881 1
0.1%
23518 1
0.1%
11616 1
0.1%
9524 1
0.1%
9318 1
0.1%
9221 1
0.1%
8680 1
0.1%

%Triple Viral
Real number (ℝ)

High correlation 

Distinct1078
Distinct (%)96.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean87.166203
Minimum0
Maximum247.70642
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2025-04-10T09:00:31.348770image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.224861
Q170.599613
median85.826772
Q3100.96526
95-th percentile130.20938
Maximum247.70642
Range247.70642
Interquartile range (IQR)30.365642

Descriptive statistics

Standard deviation26.189668
Coefficient of variation (CV)0.30045668
Kurtosis2.9334927
Mean87.166203
Median Absolute Deviation (MAD)15.153621
Skewness0.76653214
Sum97538.981
Variance685.89873
MonotonicityNot monotonic
2025-04-10T09:00:31.411179image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.5 3
 
0.3%
89.18918919 3
 
0.3%
70.3125 2
 
0.2%
56.73076923 2
 
0.2%
83.53658537 2
 
0.2%
97.56097561 2
 
0.2%
80 2
 
0.2%
54.43037975 2
 
0.2%
97.6744186 2
 
0.2%
70.96774194 2
 
0.2%
Other values (1068) 1097
97.9%
ValueCountFrequency (%)
0 2
0.2%
12.90322581 1
0.1%
14.50617284 1
0.1%
15.47619048 1
0.1%
20.68965517 1
0.1%
24.95049505 1
0.1%
25.22796353 1
0.1%
26.31578947 1
0.1%
26.58227848 1
0.1%
26.8115942 1
0.1%
ValueCountFrequency (%)
247.706422 1
0.1%
207.881137 1
0.1%
200 1
0.1%
197.1843003 1
0.1%
194.6808511 1
0.1%
185.9304085 1
0.1%
182.5825826 1
0.1%
180.5263158 1
0.1%
178.440367 1
0.1%
174.3589744 1
0.1%

Interactions

2025-04-10T09:00:27.476285image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:16.831639image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:17.591819image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:18.268809image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:19.011802image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:19.894930image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:20.959280image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:21.730748image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:22.535204image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:23.249672image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:23.924745image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:24.844125image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:25.453761image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:26.075430image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:26.773541image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:27.517767image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:16.905224image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:17.635978image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
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2025-04-10T09:00:23.121879image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
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2025-04-10T09:00:24.588902image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:25.375291image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:25.992867image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:26.683637image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:27.394769image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:28.080770image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:17.546945image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:18.225584image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:18.968398image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:19.833328image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:20.731961image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:21.674467image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:22.491366image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:23.165958image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:23.875277image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:24.628874image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:25.413352image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:26.033682image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:26.728870image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2025-04-10T09:00:27.434927image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Correlations

2025-04-10T09:00:31.456870image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
% DPT% Hepatitis B% VOP%BCG%Hib%Triple ViralBCGCODIGODPTDPTOHepatitis BHibPoblación de 1 año (1)Población menor de 1 año (1)Triple ViralVOP
% DPT1.0000.9930.9940.5340.9990.8360.3560.0340.4320.1930.4300.4320.1810.1810.3880.431
% Hepatitis B0.9931.0000.9870.5320.9920.8260.3550.0390.4310.1970.4340.4310.1820.1810.3860.430
% VOP0.9940.9871.0000.5310.9930.8350.3570.0250.4330.2020.4320.4340.1840.1840.3900.435
%BCG0.5340.5320.5311.0000.5340.5220.7630.0800.5860.2100.5860.5860.4700.4720.5800.586
%Hib0.9990.9920.9930.5341.0000.8350.3560.0350.4320.1900.4310.4330.1820.1820.3880.432
%Triple Viral0.8360.8260.8350.5220.8351.0000.3400.0400.3820.1800.3800.3820.1650.1650.4060.382
BCG0.3560.3550.3570.7630.3560.3401.0000.0050.9390.4920.9380.9390.9160.9170.9400.939
CODIGO0.0340.0390.0250.0800.0350.0400.0051.000-0.0250.968-0.025-0.025-0.039-0.037-0.024-0.026
DPT0.4320.4310.4330.5860.4320.3820.939-0.0251.0000.4890.9991.0000.9530.9530.9891.000
DPTO0.1930.1970.2020.2100.1900.1800.4920.9680.4891.0000.4890.4890.4870.4870.4890.489
Hepatitis B0.4300.4340.4320.5860.4310.3800.938-0.0250.9990.4891.0000.9990.9530.9530.9870.999
Hib0.4320.4310.4340.5860.4330.3820.939-0.0251.0000.4890.9991.0000.9530.9530.9890.999
Población de 1 año (1)0.1810.1820.1840.4700.1820.1650.916-0.0390.9530.4870.9530.9531.0001.0000.9580.953
Población menor de 1 año (1)0.1810.1810.1840.4720.1820.1650.917-0.0370.9530.4870.9530.9531.0001.0000.9580.953
Triple Viral0.3880.3860.3900.5800.3880.4060.940-0.0240.9890.4890.9870.9890.9580.9581.0000.989
VOP0.4310.4300.4350.5860.4320.3820.939-0.0261.0000.4890.9990.9990.9530.9530.9891.000

Missing values

2025-04-10T09:00:28.265692image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-10T09:00:28.380501image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-10T09:00:28.588605image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CODIGODPTOMunicipioPoblación menor de 1 año (1)VOP% VOPDPT% DPTBCG%BCGHepatitis B% Hepatitis BHib%HibPoblación de 1 año (1)Triple Viral%Triple Viral
05001AntioquiaMedellín2908529428.0101.17930229907.0102.82619941663.0143.24565929881.0102.73680629907.0102.8261992931431500.0107.457188
15002AntioquiaAbejorral370333.090.000000334.090.270270189.051.081081334.090.270270334.090.270270369315.085.365854
25004AntioquiaAbriaquí4330.069.76744230.069.76744216.037.20930230.069.76744230.069.7674424338.088.372093
35021AntioquiaAlejandría5374.0139.62264274.0139.62264248.090.56603874.0139.62264274.0139.6226425464.0118.518519
45030AntioquiaAmagá563400.071.047957414.073.534636243.043.161634414.073.534636414.073.534636569443.077.855888
55031AntioquiaAmalfi478494.0103.347280504.0105.439331312.065.271967504.0105.439331504.0105.439331477457.095.807128
65034AntioquiaAndes854547.064.051522550.064.402810494.057.845433547.064.051522550.064.402810853614.071.981243
75036AntioquiaAngelópolis12785.066.92913486.067.71653556.044.09448886.067.71653586.067.71653512983.064.341085
85038AntioquiaAngostura305236.077.377049237.077.704918155.050.819672237.077.704918237.077.704918299247.082.608696
95040AntioquiaAnorí319370.0115.987461372.0116.614420283.088.714734370.0115.987461372.0116.614420320356.0111.250000
CODIGODPTOMunicipioPoblación menor de 1 año (1)VOP% VOPDPT% DPTBCG%BCGHepatitis B% Hepatitis BHib%HibPoblación de 1 año (1)Triple Viral%Triple Viral
111097001VaupésMitú907602.066.372657601.066.262404679.074.862183601.066.262404601.066.262404890630.070.786517
111197161VaupésCarurú9347.050.53763457.061.29032383.089.24731257.061.29032357.061.2903238977.086.516854
111297511VaupésPacoa (Cd.)12577.061.60000072.057.60000064.051.20000072.057.60000072.057.60000012087.072.500000
111397666VaupésTaraira4132.078.04878039.095.12195134.082.92682939.095.12195139.095.1219514140.097.560976
111497777VaupésPapunaua (Cd.)312.06.4516132.06.45161310.032.2580652.06.4516132.06.451613314.012.903226
111597889VaupésYavaraté (Cd.)3735.094.59459547.0127.02702737.0100.00000047.0127.02702747.0127.0270273745.0121.621622
111699001VichadaPuerto Carreño355412.0116.056338412.0116.056338337.094.929577412.0116.056338412.0116.056338353350.099.150142
111799524VichadaLa Primavera329148.044.984802148.044.984802130.039.513678148.044.984802148.044.984802325162.049.846154
111899624VichadaSanta Rosalía11180.072.07207281.072.97297355.049.54955080.072.07207280.072.07207211184.075.675676
111999773VichadaCumaribo1032351.034.011628351.034.011628421.040.794574351.034.011628351.034.011628993493.049.647533